Image structure clustering for image quality verification of color retina images in diabetic retinopathy screening

Abstract

Reliable verification of image quality of retinal screening images is a prerequisite for the development of automatic screening systems for diabetic retinopathy. A system is presented that can automatically determine whether the quality of a retinal screening image is sufficient for automatic analysis. The system is based on the assumption that an image of sufficient quality should contain particular image structures according to a certain pre-defined distribution. We cluster filterbank response vectors to obtain a compact representation of the image structures found within an image. Using this compact representation together with raw histograms of the R, G, and B color planes, a statistical classifier is trained to distinguish normal from low quality images. The presented system does not require any previous segmentation of the image in contrast with previous work. The system was evaluated on a large, representative set of 1000 images obtained in a screening program. The proposed method, using different feature sets and classifiers, was compared with the ratings of a second human observer. The best system, based on a Support Vector Machine, has performance close to optimal with an area under the ROC curve of 0.9968.

DOI: 10.1016/j.media.2006.09.006

Extracted Key Phrases

9 Figures and Tables

01020302008200920102011201220132014201520162017
Citations per Year

133 Citations

Semantic Scholar estimates that this publication has 133 citations based on the available data.

See our FAQ for additional information.

Cite this paper

@article{Niemeijer2006ImageSC, title={Image structure clustering for image quality verification of color retina images in diabetic retinopathy screening}, author={Meindert Niemeijer and Michael D. Abr{\`a}moff and Bram van Ginneken}, journal={Medical image analysis}, year={2006}, volume={10 6}, pages={888-98} }